Cyber-aggression Detection using Cross Segment-and-Concatenate Multi-Task Learning from Text
Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Qianjia Huang, Diana Inkpen, David Van Bruwaene
Abstract
In this paper, we propose a novel deep-learning architecture for text classification, named cross segment-and-concatenate multi-task learning (CSC-MTL). We use CSC-MTL to improve the performance of cyber-aggression detection from text. Our approach provides a robust shared feature representation for multi-task learning by detecting contrasts and similarities among polarity and neutral classes. We participated in the cyber-aggression shared task under the team name uOttawa. We report 59.74% F1 performance for the Facebook test set and 56.9% for the Twitter test set, for detecting aggression from text.- Anthology ID:
- W18-4419
- Volume:
- Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Venue:
- TRAC
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 159–165
- Language:
- URL:
- https://aclanthology.org/W18-4419
- DOI:
- Cite (ACL):
- Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Qianjia Huang, Diana Inkpen, and David Van Bruwaene. 2018. Cyber-aggression Detection using Cross Segment-and-Concatenate Multi-Task Learning from Text. In Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), pages 159–165, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Cyber-aggression Detection using Cross Segment-and-Concatenate Multi-Task Learning from Text (Husseini Orabi et al., TRAC 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-4419.pdf